using Distributions
using Compat.Test


const n_samples = 5_000_001

mu = [1.0, 2.0, 3.0]
C = [4. -2. -1.; -2. 5. -1.; -1. -1. 6.]
h = mu
J = C


for d in [
	Dirichlet(3, 2.0), 
	Dirichlet([2.0, 1.0, 3.0]), 
	IsoNormal(mu, 2.0), 
	DiagNormal(mu, [1.5, 2.0, 2.5]), 
	MvNormal(mu, C), 
	IsoNormalCanon(h, 2.0), 
	DiagNormalCanon(h, [1.5, 2.0, 1.2]), 
	MvNormalCanon(h, J)]

	println(d)
	dmean = mean(d)
	dcov = cov(d)
	dent = entropy(d)

	x = rand(d, n_samples)
	xmean = vec(mean(x, 2))
	z = x .- xmean
	xcov = (z * z') * (1 / n_samples)

	lp = logpdf(d, x)
	xent = -mean(lp)

	println("expected mean  = $dmean")
	println("empirical mean = $xmean")
	println("--> abs.dev = $(maximum(abs(dmean - xmean)))")

	println("expected cov  = $dcov")
	println("empirical cov = $xcov")
	println("--> abs.dev = $(maximum(abs(dcov - xcov)))")

	println("expected entropy = $dent")
	println("empirical entropy = $xent")
	println("--> abs.dev = $(abs(dent - xent))")

	println()
end
